Data‐Driven Atomic Environment Prediction for Binaries Using the Mendeleev Number. Part 1. Composition AB.

Villars, P.; Cenzual, K.; Daams, J.; Chen, Y.; Iwata, Shuichi

doi:10.1002/chin.200423004

Cited by 6 publications

(8 citation statements)

References 1 publication

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“…Previous studies have mainly focused on structure types, inferring coordination environments from the type-defining structure prototype, 29,30 or on simple statements of preferential coordination numbers 31 based on very simplistic rules such as the maximum gap rule and the packing factor of the structures. 32,33 Moreover, all the methods used in the above-mentioned studies are very sensitive to small distortions in the structure.…”

Section: ■ Introductionmentioning

confidence: 99%

Statistical Analysis of Coordination Environments in Oxides

et al. 2017

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Coordination or local environments (e.g., tetrahedra and octahedra) are powerful descriptors of the crystalline structure of materials. These structural descriptors are essential to the understanding of crystal chemistry and the design of new materials. However, extensive statistics on the occurrence of local environment are not available even on common chemistries such as oxides. Here, we present the first large-scale statistical analysis of the coordination environments of cations in oxides using a large set of experimentally observed compounds (about 8000). Using a newly developed method, we provide the distribution of local environment for each cation in oxides. We discuss our results highlighting previously known trends and unexpected coordination environments, as well as compounds presenting very rare coordinations. Our work complements the know-how of the solid state chemist with a statistically sound analysis and paves the way for further data mining efforts linking, for instance, coordination environments to materials properties.

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Section: ■ Introductionmentioning

confidence: 99%

Statistical Analysis of Coordination Environments in Oxides

et al. 2017

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“…These category B and C compounds are of particular interest since their synthesizability cannot be modeled with DFT stability alone. The third most important feature, Mendeleev number, is an intuitive feature since it orders elements by chemical similarity [63]. Other top features such as covalent radii and electron counts are also intuitively important, though our model cannot provide specific and interpretable rules that relate these features with synthesizability.…”

Section: Feature Discussionmentioning

confidence: 99%

Machine Learned Synthesizability Predictions Aided by Density Functional Theory

Lee¹,

Sarker²,

Saal³

et al. 2022

Preprint

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A grand challenge of materials science is the computational prediction of synthesis pathways for novel compounds. Data-driven and machine learning (ML) approaches have made significant progress in addressing a portion of this problem, namely, predicting whether a compound is synthesizable or not. However, some recent attempts to do so have not incorporated energetic or phase stability information. Here, we combine thermodynamic stability calculated using density functional theory (DFT) with composition-based features to train a ML model that predicts a material's synthesizability. We focus on compositions with ABC stoichiometry and predict synthesizability in the half-heusler (HH) structure. Our model is trained on experimentally reported ABC compositions, and achieves a cross-validated precision of 0.83 and recall of 0.85. Our model shows improvement in identifying compositions that do not form HH when compared to a similar HH synthesizability model from a previous study. Our model identifies 163 synthesizable candidates out of 4141 unreported ABC compositions. More notably, 38 stable compositions are predicted unsynthesizable while 103 unstable compositions are predicted synthesizable; these findings otherwise cannot be made using DFT stability alone. This study presents a new approach for accurately predicting synthesizability, and identifies newly synthesizable candidate HH compositions for further experimental exploration.

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“…29,30 Governing the structure-property relationship, structure motifs or coordination environments can be viewed as effective structural descriptors for crystals. The efforts for identification of local coordination environments initially focused on structure types 31,32 or preferential coordination numbers 33 based on simple rules. 34,35 Very recently, owing to the development of data-driven approaches, systematic and robust approaches to automatically identify local environments have been developed, 36,37 which motivated the use of structure motif information for material design in a data-driven paradigm.…”

Section: Introductionmentioning

confidence: 99%

Structure motif–centric learning framework for inorganic crystalline systems

et al. 2021

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Incorporation of physical principles in a machine learning (ML) architecture is a fundamental step toward the continued development of artificial intelligence for inorganic materials. As inspired by the Pauling’s rule, we propose that structure motifs in inorganic crystals can serve as a central input to a machine learning framework. We demonstrated that the presence of structure motifs and their connections in a large set of crystalline compounds can be converted into unique vector representations using an unsupervised learning algorithm. To demonstrate the use of structure motif information, a motif-centric learning framework is created by combining motif information with the atom-based graph neural networks to form an atom-motif dual graph network (AMDNet), which is more accurate in predicting the electronic structures of metal oxides such as bandgaps. The work illustrates the route toward fundamental design of graph neural network learning architecture for complex materials by incorporating beyond-atom physical principles.

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Data‐Driven Atomic Environment Prediction for Binaries Using the Mendeleev Number. Part 1. Composition AB.

Cited by 6 publications

References 1 publication

Statistical Analysis of Coordination Environments in Oxides

Statistical Analysis of Coordination Environments in Oxides

Machine Learned Synthesizability Predictions Aided by Density Functional Theory

Structure motif–centric learning framework for inorganic crystalline systems

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